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Abstract: Building managers have specific duties and certain outputs that are required of them. Without the necessary data, information, tools, and time, they are unable to adequately meet their organisational goals. Scenario modelling enables explicit and unambiguous coupling of building functions with other pivotal aspects of building operation in a method that specifically considers the education and technical expertise of building managers. This new method captures, transforms, and communicates the complex interdependencies of environmental and energy management in buildings through an easily navigable, holistic, and reproducible checking mechanism that compares actual performance with predicted performance and completes the “plan-do-check-act” cycle for building managers. Most important, the structured nature of this method caters to the diverse profile of building managers, making it applicable for widespread deployment. This paper demonstrates the benefit of using the new method by examining its application to a performance analysis of two existing buildings. [Copyright &y& Elsevier]

Abstract: This paper presents the key factors methodology that supports energy managers in determining the optimal building operation strategy in relation to both energy consumption and thermal comfort. The methodology is supported by the utilization of calibrated building energy simulation models that match measured data gathered by an extensive measurement framework. Building management systems, do not allow energy managers to test the impact of changes in control settings of energy systems on energy consumption and thermal comfort. The proposed methodology and prototype tool chain enables energy managers to virtually develop and test the impact of proposed changes to the control settings in the building energy systems prior to their implementation in the physical building. The paper outlines the proposed methodology defining the underpinning concepts and illustrating the performance metrics required to capture the effect of different building operation strategies. A case study is discussed to demonstrate the application of the methodology. [Copyright &y& Elsevier]

• A novel method based on clustering, building energy models and Bayesian techniques is presented. • The approach enables the calibration of model parameters for large groups of buildings at an urban scale. • Various data-driven techniques have been tested acting as proxy of the simulation engine. • Three different surrogate structures have been investigated with comparison of the results. • The calibrated models provide realistic results compared to current literature studies. Prediction of building energy use, when performed at urban scale, is influenced by the choice of modelling approach, as well as the quality of available data. In the case of data scarcity, one of the main limitations of current urban scale building energy simulation models, is the use of deterministic approaches to specify modelling inputs for entire classes of buildings. This modelling approach is characterised by three major shortcomings: first, data uncertainties are not comprehensively considered; second, a rigorous method of identification of groups of buildings to be populated by similar modelling parameters is not specified; and third, strategies to calibrate the developed energy models are missing. Considering these challenges, the current paper presents a non-deterministic calibration method for groups of buildings. The methodology utilises four techniques: (i) the use of clustering to identify building groups and associated representative buildings within the urban context; (ii) the application of an automatic building energy modelling approach to simulate buildings within these groups; (iii) the application of data-driven models, used as emulators of the dynamic simulation engine, in a computationally efficient manner; and (iv) the exploitation of a non-deterministic Bayesian calibration framework to identify sets of representative parameters for the building clusters. Datasets, based on 2646 buildings from the city of Geneva, Switzerland, are used as a case study. Validation with measured data, shows that energy consumption and energy intensity predictions when considered at an aggregated scale, are within +/– 5%. On an individual building basis, the validation error is within +/– 20% for approximately 70% of the buildings. [ABSTRACT FROM AUTHOR]

• Evaluation of existing approaches or projects of building archetype. • Developing a generalized multi-scale building archetype methodology. • Implementing the data-driven approaches for the characterization of building archetypes. • Comparison of the modeling results of multi-scale building archetype at BEM and UBEM levels. Globally the building sector accounts for a significant portion of the overall energy demand and greenhouse gas emissions of any country. The most common approach for the collection of modeling and benchmarking data that can be used for predictions of energy performance at a national or urban scale is through classification of the building stock into representative archetypes. Developing such building archetypes is a complex task due to the difficulties associated with gathering detailed geometric and non-geometric data at an urban scale. Although existing databases and projects provide a valuable overview of a building stock, the information about buildings' physical descriptions are not regularly updated. Moreover, these databases cover only the national top-level archetypes and lack crucial information related to city or district scale building stocks. The use of national scale archetypes requires many assumptions that may not hold true for energy modeling at urban or district scale. This paper proposes a multi-scale (national, city, county and district) archetype development methodology using different data-driven approaches. The methodology consists of following five steps: 1) data collection, 2) segmentation, 3) characterization, 4) quantification, and 5) modeling results. We developed a test case based on the available building stock data of Ireland. The test case used previously developed archetype geometries coupled with the parameters determined by the characterization process to calculate annual energy use (kWh) of buildings at a multiple-scales. The resulting archetypes at national, city, county and district scale are analyzed and compared against one another. The results indicate that significant differences occur in terms of energy modeling results when national scale archetypes are used to simulate the energy performance of buildings at the local scale. These multi-scale building archetypes will aid local authorities and city planners when analyzing energy efficiency and consequently, help to improve sustainable energy policy decisions. [ABSTRACT FROM AUTHOR]